class_docstr: Gaussian Kernel Density Estimation.
  
  Args:
      dataset (Tensor | None, optional): Dataset on which to fit the KDE model. Defaults to None.
code: def __init__(self, dataset: Tensor | None = None):
      super().__init__()
  
      if dataset is not None:
          self.fit(dataset)
  
      self.register_buffer("bw_transform", Tensor())
      self.register_buffer("dataset", Tensor())
      self.register_buffer("norm", Tensor())
  
      self.bw_transform = Tensor()
      self.dataset = Tensor()
      self.norm = Tensor()
  
file_path: anomalib/src/anomalib/models/components/stats/kde.py
imports: from __future__ import annotations
  import math
  import torch
  from torch import Tensor
  from anomalib.models.components.base import DynamicBufferModule
label_desc: Use image node_id 1 to create a new GaussianKDE object from inherited base classes: DynamicBufferModule with example: obj = GaussianKDE(dataset)